Teaching

I am responsible for the following courses:

  1. Applied Machine Learning and Data Mining CM1001
  2. Applied Machine Learning and Data Mining for Performance Analysis CM2007
  3. Applied Machine Learning and Artificial Intelligence CM2011

PhD Students

  1. Arsineh Boodaghian Asl
  2. Luca Marzano
  3. Merja Hietanen
  4. Mikaela Hellstrand

Master’s Thesis Students

2024

  1. Tova Eivinsson
  2. Saman Bozorgi
  3. Julian Karwacki
  4. Rita Costa

2023

  1. Carrera Jeri, P. (2023). Risk Stratification of Endometriosis through Machine Learning using Lifestyle Data: An Extensive Analysis on Lifestyle Data to Reveal Patterns in People with Endometriosis (Issue 2023:048).

2022

  1. Jefford-Baker, B. (2022). Autonomous Patient Monitoring in the Intermediate Care Unit by Live Video Analysis (Issue 2022:104).
  2. Lindberg, T. (2022). Early Detection and Differentiation of Circulatory Shock in the Intensive Care Unit using Machine Learning (Issue 2022:009).
  3. Malm, E. (2022). Machine Learning for Early Prediction of Pneumothorax in the Intensive Care Unit (Issue 2022:010).
  4. Rosamilia, U. (2022). Applying Nonlinear Mixed-Effects Modeling to Model Patient Flow in the Emergency Department: Evaluation of the Impact of Patient Characteristics on Emergency Department Logistics (Issue 2022:098).

2019

  1. Wadhwa, R. (2019). Systems mapping ofwork-stress mental health inStockholm to inform policydecision making (Issue 2019:134).

2018

  1. Skoglund, P., & Peterson, T. (2018). Development of a Simulation Platform Addressing the Digitalization of the Stockholm Healthcare System (Issue 2018:26).
  2. Dizdarevic, S., & Hämäläinen, A. (2018). Developing a simulation model for decision making in a further digitized Swedish healthcare system (Issue 2018:110).

2017

  1. Nilsson Hall, R., & Jerjas, A. (2017). Specifying an ontology framework to model processes in hospitals (Issue 2017:26).